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Katie A. Wilson, Pamela L. Heinselman, and Ziho Kang

Abstract

Eye-tracking technology can observe where and how someone’s eye gaze is directed, and therefore provides information about one’s attention and related cognitive processes in real time. The use of eye-tracking methods is evident in a variety of research domains, and has been used on few occasions within the meteorology community. With the goals of Weather Ready Nation in mind, eye-tracking applications in meteorology have so far supported the need to address how people interpret meteorological information through televised forecasts and graphics. However, eye tracking has not yet been applied to learning about forecaster behavior and decision processes. In this article, we consider what current methods are being used to study forecasters and why we believe eye tracking is a method that should be incorporated into our efforts. We share our first data collection of an NWS forecaster’s eye gaze data, and explore the types of information that these data provide about the forecaster’s cognitive processes. We also discuss how eye-tracking methods could be applied to other aspects of operational meteorology research in the future, and provide motivation for further exploration on this topic.

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Katie A. Wilson, Pamela L. Heinselman, and Ziho Kang

Abstract

An eye-tracking experiment was conducted to examine whether differences in forecasters’ eye movements provide further insight into how radar update speed impacts their warning decision process. In doing so, this study also demonstrates the applications of a new research method for observing how National Weather Service forecasters distribute their attention across a radar display and warning interface. In addition to observing forecasters’ eye movements during this experiment, video data and retrospective recalls were collected. These qualitative data were used to provide an explanation for differences observed in forecasters’ eye movements. Eye movement differences were analyzed with respect to fixation measures (i.e., count and duration) and scanpath dimensions (i.e., vector, direction, length, position, and duration). These analyses were completed for four stages of the warning decision process: the first 5 min of the case, 2 min prior to warning decisions, the warning issuance process, and warning updates. While radar update speed did not impact forecasters’ fixation measures during these four stages, comparisons of scanpath dimensions revealed differences in their eye movements. Video footage and retrospective recall data illustrated how forecasters’ interactions with the radar display and warning interface, encounters with technological challenges, and varying approaches to similar tasks resulted in statistically significantly (p value < 0.05) lower scanpath similarity scores. The findings of this study support the combined use of eye-tracking and qualitative research methods for detecting and understanding individual differences in forecasters’ eye movements. Future applications of these methods in operational meteorology research have potential to aid usability studies and improve human–computer interactions for forecasters.

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Katie A. Wilson, Pamela L. Heinselman, and Charles M. Kuster

Abstract

Thirty National Weather Service forecasters worked with 1-, 2-, and 5-min phased-array radar (PAR) volumetric updates for a variety of weather events during the 2015 Phased Array Radar Innovative Sensing Experiment. Exposure to each of these temporal resolutions during simulated warning operations meant that these forecasters could provide valuable feedback on how rapidly updating PAR data impacted their warning decision processes. To capture this feedback, forecasters participated in one of six focus groups. A series of open-ended questions guided focus group discussions, and forecasters were encouraged to share their experiences and opinions from the experiment. Transcriptions of focus group discussions were thematically analyzed, and themes belonging to one of two groups were identified: 1) forecasters’ use of rapidly updating PAR data during the experiment and 2) how forecasters envision rapidly updating PAR data being integrated into warning operations. Findings from this thematic analysis are presented in this paper, and to illustrate these findings from the forecasters’ perspectives, dialogue that captures the essence of their discussions is shared. The identified themes provide motivation to integrate rapidly updating radar data into warning operations and highlight important factors that need to be addressed for the successful integration of these data.

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Katie A. Wilson, Pamela L. Heinselman, Charles M. Kuster, Darrel M. Kingfield, and Ziho Kang

Abstract

Impacts of radar update time on forecasters’ warning decision processes were analyzed in the 2015 Phased Array Radar Innovative Sensing Experiment. Thirty National Weather Service forecasters worked nine archived phased-array radar (PAR) cases in simulated real time. These cases presented nonsevere, severe hail and/or wind, and tornadic events. Forecasters worked each type of event with approximately 5-min (quarter speed), 2-min (half speed), and 1-min (full speed) PAR updates. Warning performance was analyzed with respect to lead time and verification. Combining all cases, forecasters’ median warning lead times when using full-, half-, and quarter-speed PAR updates were 17, 14.5, and 13.6 min, respectively. The use of faster PAR updates also resulted in higher probability of detection and lower false alarm ratio scores. Radar update speed did not impact warning duration or size. Analysis of forecaster performance on a case-by-case basis showed that the impact of PAR update speed varied depending on the situation. This impact was most noticeable during the tornadic cases, where radar update speed positively impacted tornado warning lead time during two supercell events, but not for a short-lived tornado occurring within a bowing line segment. Forecasters’ improved ability to correctly discriminate the severe weather threat during a nontornadic supercell event with faster PAR updates was also demonstrated. Forecasters provided subjective assessments of their cognitive workload in all nine cases. On average, forecasters were not cognitively overloaded, but some participants did experience higher levels of cognitive workload at times. A qualitative explanation of these particular instances is provided.

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Nusrat Yussouf, Katie A. Wilson, Steven M. Martinaitis, Humberto Vergara, Pamela L. Heinselman, and Jonathan J. Gourley

Abstract

The goal of the National Oceanic and Atmospheric Administration’s (NOAA) Warn-on-Forecast (WoF) program is to provide frequently updating, probabilistic model guidance that will enable National Weather Service (NWS) forecasters to produce more continuous communication of hazardous weather threats (e.g., heavy rainfall, flash floods, damaging wind, large hail, and tornadoes) between the watch and warning temporal and spatial scales. To evaluate the application of this WoF concept for probabilistic short-term flash flood prediction, the 0–3-h rainfall forecasts from NOAA National Severe Storms Laboratory’s (NSSL) experimental WoF System (WoFS) were integrated as the forcing to the NWS operational hydrologic modeling core within the Flooded Locations and Simulated Hydrographs (FLASH) system. Initial assessment of the potential impacts of probabilistic short-term flash flood forecasts from this coupled atmosphere–hydrology (WoFS-FLASH) modeling system were evaluated in the 2018 Hydrometeorology Testbed Multi-Radar Multi-Sensor Hydrology experiment held in Norman, Oklahoma. During the 3-week experiment period, a total of nine NWS forecasters analyzed three retrospective flash flood events in archive mode. This study will describe specifically what information participants extracted from the WoFS-FLASH products during these three archived events, and how this type of information is expected to impact operational decision-making processes. Overall feedback from the testbed participants’ evaluations show promise for the coupled NSSL WoFS-FLASH system probabilistic flash flood model guidance to enable earlier assessment and detection of flash flood threats and to advance the current warning lead time for these events.

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Katie A. Wilson, Pamela L. Heinselman, Patrick S. Skinner, Jessica J. Choate, and Kim E. Klockow-McClain

Abstract

During the 2017 Spring Forecasting Experiment in NOAA’s Hazardous Weather Testbed, 62 meteorologists completed a survey designed to test their understanding of forecast uncertainty. Survey questions were based on probabilistic forecast guidance provided by the NSSL Experimental Warn-on-Forecast System for ensembles (NEWS-e). A mix of 20 multiple-choice and open-ended questions required participants to explain basic probability and percentile concepts, extract information using graphical representations of uncertainty, and determine what type of weather scenario the graphics depicted. Multiple-choice questions were analyzed using frequency counts, and open-ended questions were analyzed using thematic coding methods. Of the 18 questions that could be scored, 60%–96% of the participants’ responses aligned with the researchers’ intended response. Some of the most challenging questions proved to be those requiring qualitative explanations, such as to explain what the 70th-percentile value of accumulated rainfall represents in an ensemble-based probabilistic forecast. Additionally, participants providing answers not aligning with the intended response oftentimes appeared to consider the given information with a deterministic rather than probabilistic mindset. Applications of a deterministic mindset resulted in tendencies to focus on the worst-case scenario and to modify understanding of probabilistic concepts when presented with different variables. The findings from this survey support the need for improved basic and applied training for the development, interpretation, and use of probabilistic ensemble forecast guidance. Future work should collect data for a larger sample size to examine the knowledge gaps across specific user groups and to guide development of probabilistic forecast training tools.

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Charles M. Kuster, Pamela L. Heinselman, Jeffrey C. Snyder, Katie A. Wilson, Douglas A. Speheger, and James E. Hocker

Abstract

Many public safety officials (e.g., emergency managers and first responders) use weather-radar data to inform many life-saving decisions, such as sounding outdoor warning sirens and directing storm spotters. Therefore, to include this important user group in ongoing radar applications research, a knowledge coproduction framework is used to interact with, learn from, and provide information to public safety officials. From these interactions, it became clear that radar-based products that estimate a tornado’s location, intensity, or both could be valuable to public safety officials. Therefore, a survey was conducted and a focus group formed to 1) collect feedback on several of these products currently under development, 2) identify potential decisions that could be made with these products, and 3) examine the impact of radar update time on product usefulness. An analysis of the survey and focus group responses revealed that public safety officials preferred simple interactive products provided to them using multiple communication methods. Once received, any product that could clearly communicate where a tornado may have occurred would likely help public safety officials focus search and rescue efforts in the immediate aftermath of a tornado. Additionally, public safety officials preferred products created using rapid-update data (1–2-min volumetric updates) over conventional-update data (4–5-min volumetric updates) because it provided them with more complete information.

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Alexander G. Keul, Bernhard Brunner, John Allen, Katie A. Wilson, Mateusz Taszarek, Colin Price, Gary Soleiman, Sanjay Sharma, Partha Roy, Mat Said Aini, Abu Bakar Elistina, Mohd Zainal Abidin Ab Kadir, and Chandima Gomes

ABSTRACT

Weather risk perception research lacks multihazard and transcultural datasets. This hypothesis-generating study used a cognitive behavioral approach and Brunswik’s lens model for subjective risk parameters across eight countries. In Germany, Poland, Israel, the United States, Brazil, India, Malaysia, and Australia, 812 field interviews took place with a uniform set of 37 questions about weather interest, media access, elementary meteorological knowledge, weather fear, preparedness, loss due to weather, and sociodemography. The local randomized quota samples were strictly tested for sample errors; however, they cannot be considered representative for individual countries due to sample size and methodology. Highly rated subjective risks included flood, heat, tornado, and lightning. Weather fear was most prominent in the Malaysian sample and lowest in the German.

Subjective elements were further explored with bivariate correlations and a multivariate regression analysis. Sociodemography correlated with psychological variables like knowledge, interest, and fear. Fear was related with subjective risk; less educated and informed people were more fearful. A linear regression analysis identified interest, gender, housing type, education, loss due to weather, and local weather access as the significant predictors for preparedness. The level of preparedness was highest in the United States and Australia and lowest in the Malaysian and Brazilian samples. A lack of meteorological training and infrequent loss experiences make media communication important and emphasize the value of repetition for basic information. Elements of this survey can serve to monitor weather-related psychological orientations of vulnerable population groups. Finally, this survey provides a template with which larger representative transcultural multihazard perception studies can be pursued.

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Adam J. Clark, Israel L. Jirak, Burkely T. Gallo, Brett Roberts, Kent. H. Knopfmeier, Robert A. Clark, Jake Vancil, Andrew R. Dean, Kimberly A. Hoogewind, Pamela L. Heinselman, Nathan A. Dahl, Makenzie J. Krocak, Jessica J. Choate, Katie A. Wilson, Patrick S. Skinner, Thomas A. Jones, Yunheng Wang, Gerald J. Creager, Larissa J. Reames, Louis J. Wicker, Scott R. Dembek, and Steven J. Weiss
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Adam J. Clark, Israel L. Jirak, Burkely T. Gallo, Brett Roberts, Andrew R. Dean, Kent H. Knopfmeier, Louis J. Wicker, Makenzie Krocak, Patrick S. Skinner, Pamela L. Heinselman, Katie A. Wilson, Jake Vancil, Kimberly A. Hoogewind, Nathan A. Dahl, Gerald J. Creager, Thomas A. Jones, Jidong Gao, Yunheng Wang, Eric D. Loken, Montgomery Flora, Christopher A. Kerr, Nusrat Yussouf, Scott R. Dembek, William Miller, Joshua Martin, Jorge Guerra, Brian Matilla, David Jahn, David Harrison, David Imy, and Michael C. Coniglio
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